CN113780917A

CN113780917A - Cargo scheduling method and system

Info

Publication number: CN113780917A
Application number: CN202011372500.XA
Authority: CN
Inventors: 张瑜筱丹; 郭宇飞
Original assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2020-11-30
Filing date: 2020-11-30
Publication date: 2021-12-10
Anticipated expiration: 2040-11-30
Also published as: CN113780917B

Abstract

The disclosure discloses a cargo scheduling method and a cargo scheduling system, and relates to the field of warehousing. The method comprises the following steps: acquiring the types of stock keeping units SKU related to the user order and the quantity of each type of SKU; judging whether the user order can match with the SKU combination or not according to the SKU types and the quantity of each SKU; generating one or more sorting entries according to the matching result, wherein each sorting entry comprises a SKU combination or a single SKU matched with the sorting entry; and sorting the goods according to the one or more sorting items. Because no new SKU is created, the sorting efficiency is improved, and the accuracy of SKU statistics can be improved.

Description

Cargo scheduling method and system

Technical Field

The present disclosure relates to the field of warehousing, and in particular, to a method and a system for scheduling goods.

Background

At present, the processing operation of the group goods mainly depends on a processing center system. The sales staff creates a work order, establishes a set relationship and a work quantity of set SKUs (Stock keeping Unit), for example, SKU (c) ═ 2 × SKU (a) + SKU (b), and downloads the work order to the work center system. After receiving the processing list, the processing center generates a raw material receiving list and issues a WMS (route Management Systems). And the warehouse operating personnel receives the material receiving list and finishes material receiving and delivery operations such as task allocation, goods picking and off-shelf, goods picking and converging, material receiving list delivery and the like through the WMS. After the warehouse operators operate and deliver the goods, all raw material commodities are sent to a processing center for raw material delivery. And after the raw materials are handed over, printing a processing list and a finished product label, processing the raw materials into a finished product according to the processing list, and sending finished product warehousing list information to the WMS. And the WMS stores the packaged commodities in a warehouse and puts the commodities on a shelf according to the warehouse entry number. Customers can directly see fixed set SKU and the set relation thereof on a front shopping page, the purchased goods are set SKU (C) after placing orders, and the warehouse is produced and exported according to the set SKU (C). If the commodities are sold in a sleeved mode, a plurality of different SKUs are required to be created, and the combination relationship is fixed.

Disclosure of Invention

The technical problem to be solved by the present disclosure is to provide a cargo scheduling method and system, which can improve the accuracy of SKU statistics while improving the sorting efficiency.

According to an aspect of the present disclosure, a cargo scheduling method is provided, including: acquiring the types of stock keeping units SKU related to the user order and the quantity of each type of SKU; judging whether the user order can be matched with the SKU combination or not according to the SKU type and the quantity of each SKU; generating one or more sorting entries according to the matching result, wherein each sorting entry comprises a SKU combination or a single SKU matched with the sorting entry; and sorting the goods according to the one or more sorting items.

In some embodiments, according to the user's requirement, the goods are pre-grouped to generate a plurality of grouped goods, each grouped goods corresponds to a SKU combination, and a SKU combination comprises the type of SKU and the quantity of each SKU.

In some embodiments, if the user order can match a plurality of SKU combinations, the SKU combination which meets the user order requirement and contains the maximum SKU number in the plurality of SKU combinations is preferentially used as the SKU combination matched with the user order; and judging whether the SKU combination which meets the residual goods requirement in the user order and contains the maximum SKU quantity exists in the plurality of SKU combinations except the SKU combination matched with the user order, if so, taking the SKU combination which meets the residual goods requirement in the user order and contains the maximum SKU quantity as the SKU combination matched with the user order, and continuing to execute the step of judging whether the SKU combination which meets the residual goods requirement in the user order and contains the maximum SKU quantity exists in the plurality of SKU combinations except the SKU combination matched with the user order, otherwise, taking the SKU corresponding to the residual goods in the user order as the single SKU.

In some embodiments, according to the matched one or more SKU combinations, determining the area of the set of goods corresponding to each SKU combination; and determining the storage position of each group of goods according to at least one of the sequence of the production date of each group of goods from morning to evening and the sequence of the storage position inventory from small to large in the area of each group of goods.

In some embodiments, determining the number of parcels corresponding to the user order according to the sum of the parcel coefficient of the group of the matched one or more SKU combinations corresponding to each SKU combination and the parcel coefficient of the single-product goods corresponding to each single-product SKU; and packaging the goods in the user order according to the number of the packages corresponding to the user order.

In some embodiments, the packaging factor for a package is the ratio of the package volume of the package to the default consumable volume.

According to another aspect of the present disclosure, there is also provided a cargo scheduling system, including: a SKU determination unit configured to acquire a SKU type of stock keeping unit and a quantity of each SKU involved in the user order; the combination matching unit is configured to judge whether the user order can match the SKU combination according to the SKU type and the quantity of each SKU; a sorting item generating unit configured to generate one or more sorting items according to the matching result, wherein each sorting item comprises a SKU combination or a single SKU matched with the sorting item; and a sorting unit configured to sort the goods according to the one or more sort items.

In some embodiments, the cargo scheduling system further comprises: the pre-configuration unit is configured to pre-group the goods according to user requirements to generate a plurality of group goods, each group goods corresponds to a SKU combination, and each SKU combination comprises the type of the SKU and the quantity of each SKU.

In some embodiments, the cargo scheduling system further comprises: the parcel number determining unit is configured to determine the parcel number corresponding to the user order according to the sum of the parcel coefficient of the group of goods corresponding to each SKU combination and the parcel coefficient of the single goods corresponding to each single SKU in the matched one or more SKU combinations; and the packaging unit is configured to package the goods in the user order according to the number of the packages corresponding to the user order.

According to another aspect of the present disclosure, there is also provided a cargo scheduling system, including: a memory; and a processor coupled to the memory, the processor configured to perform the cargo scheduling method as described above based on instructions stored in the memory.

According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is also proposed, on which computer program instructions are stored, which instructions, when executed by a processor, implement the above-mentioned cargo scheduling method.

In the embodiment of the disclosure, whether the SKU combination can be matched is judged according to the SKU type and the quantity of each SKU related to the user order, then one or more sorting items are generated according to the matching result, each sorting item comprises the matched SKU combination or single-product SKU, goods are sorted according to the one or more sorting items, and the accuracy of SKU statistics can be improved while the sorting efficiency is improved because no new SKU is created again.

Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.

The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:

fig. 1 is a flow diagram of some embodiments of a cargo scheduling method of the present disclosure.

Fig. 2 is a flowchart illustrating further embodiments of the cargo scheduling method according to the present disclosure.

Fig. 3 is a flow diagram of some embodiments of a stack production process of the present disclosure.

Fig. 4 is a schematic view of virtual package merchandise information according to the present disclosure.

FIG. 5 is a schematic diagram of a create stack task interface of the present disclosure.

FIG. 6 is a schematic diagram of a stack task assignment interface according to the present disclosure.

Fig. 7 is a raw material commodity shelf interface schematic of the present disclosure.

Fig. 8 is a raw material commodity shelf interface schematic of the present disclosure.

Fig. 9 is a schematic view of a stack cargo racking interface of the present disclosure.

Fig. 10 is a schematic view of a stack loading interface according to the present disclosure.

Fig. 11 is a schematic view of a pick-up interface according to the present disclosure.

Fig. 12 is a schematic structural diagram of some embodiments of the cargo scheduling system of the present disclosure.

Fig. 13 is a schematic structural diagram of another embodiment of the cargo scheduling system of the present disclosure.

Fig. 14 is a schematic structural diagram of another embodiment of the cargo scheduling system of the present disclosure.

Detailed Description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.

Shadow individual goods are mainly used in different sales promotion and different quotation scenes, one shadow individual goods is copied from a master product, different gifts are bound to the same master product of group goods, namely shadow individual logic is adopted, and a plurality of different SKUs need to be created by a sales person, so that the distribution of the SKUs of the master product is easily caused. But if each order is sorted according to a single SKU, the shipment is inefficient.

Fig. 1 is a flow diagram of some embodiments of a cargo scheduling method of the present disclosure. This embodiment is performed by WMS.

At step 110, the type of SKU and the quantity of each SKU involved in the user order are obtained.

In this embodiment, the WMS receives a single SKU order, rather than a nested SKU order. For example, the user order includes item A and item B, rather than package C containing item A and item B, the user order relates to 1 SKU (A) and 1 SKU (B).

At step 120, a determination is made as to whether the user order can match the SKU combination based on the SKU type and the quantity of each SKU.

In some embodiments, if the user order relates to SKU (A) and SKU (B), wherein the number of SKUs (A) is 2 and the number of SKU (B) is 1. If a plurality of SKU combinations, for example, SKU combinations including SKU (A) + SKU (B), are generated when the set task is created in advance, it can be determined that the user order can match the SKU combination of SKU (A) + SKU (B).

In step 130, one or more sortation entries are generated based on the match, each sortation entry including a SKU combination or an individual SKU that is matched.

For example, the user order relates to SKU (A) and SKU (B), where the quantity of SKU (A) is 2 and the quantity of SKU (B) is 1. If the user order matches a SKU combination of SKU (A) + SKU (B) and a SKU (A), two sortation entries are generated, one sortation entry corresponding to the SKU combination of SKU (A) + SKU (B) and the other sortation entry corresponding to SKU (A). If the user order matches a SKU combination of 2 × SKU (A) + SKU (B), a sort entry is generated, which corresponds to the SKU combination of 2 × SKU (A) + SKU (B). And if the user order does not match the SKU combination, generating two sorting items, wherein one sorting item corresponds to two SKUs (A) and the other sorting item corresponds to a SKU (B).

At step 140, the goods are sorted based on the one or more sort entries.

In the above embodiment, it is determined whether the SKU combination can be matched according to the SKU type and the number of each SKU involved in the user order, then one or more sorting entries are generated according to the matching result, each sorting entry includes a matched SKU combination or an individual SKU, and sorting of the goods is performed according to the one or more sorting entries, and since no new SKU is created again, the accuracy of SKU statistics can be improved while the sorting efficiency is improved.

Fig. 2 is a flowchart illustrating further embodiments of the cargo scheduling method according to the present disclosure. This embodiment is performed by WMS.

In step 210, according to the user's requirement, the goods are pre-packaged to generate a plurality of packaged goods, each packaged goods corresponds to a SKU combination, and a SKU combination includes the type of SKU and the number of each SKU.

In some embodiments, the stack production process is as shown in fig. 3.

At step 310, a virtual suite storage area is created.

In some embodiments, a new storage type "virtual stack" is added in WMS for system level positioning of stack cargo storage bay; and dividing an area in the warehouse as a virtual stack storage area for storing virtual stack goods on a physical layer.

At step 320, package basis data is maintained. As shown in fig. 4, the basic data of the set of goods includes the corresponding relationship between the matched set of goods and the raw material composition thereof.

At step 330, a stack task is created and a stack task assignment is performed.

In some embodiments, as shown in fig. 5, the goods and quantities of the required set to be created are selected according to the forecast combinations and single quantities issued by the sales staff and the maintained basic data, and the stock of the raw materials is manually positioned to create the set task. A package of goods corresponds to a SKU combination that is not a new SKU.

In some embodiments, as shown in FIG. 6, the already created task stack is selected for task assignment.

At step 340, the raw material cargo is off-shelved.

In some embodiments, as shown in fig. 7 and 8, the picker uses the raw material off-shelf function to pick up the assigned package assignments, bind the container numbers, and order the manually located raw material SKUs. For example, raw material SKUs are off-shelved into containers and transferred through the containers to the stack processing area.

At step 350, the raw material set is palletized.

In some embodiments, the raw materials are grouped, packaged, and coded according to a group list and rules issued by the sales staff.

At step 360, the package is shelved.

In some embodiments, as shown in fig. 9 and 10, the picker uses the package racking function to scan the container number, display the package task to be racked in the container, scan the package barcode, confirm the racking quantity and storage location, and complete the package racking.

In the embodiment, the intermediate links from the processing center to the warehouse of the raw materials and the finished products are reduced by the mode of pre-packaging production in the warehouse, and the processing period is shortened, so that the production efficiency is improved.

In step 220, the type of SKU and the quantity of each SKU involved in the user order are obtained.

At step 230, a determination is made as to whether the user order can match the SKU combination based on the SKU type and the quantity of each SKU.

In some embodiments, a determination is made as to whether the SKU in the user's order can match the virtual package inventory maintained in the base data. If a user order can be matched to at least one package good, the user order is marked as a "virtual package".

In this embodiment, the order combination is calculated according to the "stock surplus minimization principle", that is, the number of remaining SKUs is minimized after the SKU combination containing the largest number of SKUs is selected, so that the sorting items are minimized, and the goods sorting efficiency is improved. For example: the goods in the user order comprise a plurality of SKUs ABCDEFGH, and the combination 1(ABCD), the combination 2(ABC), the combination 3(BCD), the combination 4(EFG) and the combination 5(EF) exist in the warehouse. At this time, the virtual stack with the largest number of products, i.e. combination 1(ABCD), is preferably selected, and then the virtual stack with the largest number of products, i.e. combination 4(EFG), is selected from the excess stock (EFGH), so that the order is divided into the ABCD box, EFG box and H single products, i.e. the excess stock is the smallest.

In step 240, according to the matched one or more SKU combinations, determining the area of the set goods corresponding to each SKU combination; and determining the storage position of each group of goods according to the sequence of the production date of each group of goods from morning to evening, or the sequence of the storage position stock from small to large, or the combination of the two. The date of manufacture of the package is, for example, the date of the SKU with the earliest date of manufacture in the stock of the package.

In some embodiments, if the user order includes virtual package goods, the combination with less storage space stock is preferentially positioned according to the principle of satisfying the priority emptying storage space; and the principle of preferentially positioning the set box with the earliest quality guarantee period of the set of the goods is met on the basis of first-in first-out principle, so that the goods are positioned. The stack cargo is located in the virtual stack storage area and the non-stack cargo is located in the common storage area.

In step 250, the parcel number corresponding to the user order is determined according to the sum of the parcel coefficient of the group goods corresponding to each SKU combination and the parcel coefficient of the single goods corresponding to each single SKU in the matched one or more SKU combinations.

In some embodiments, during parcel production, the order parcel coefficients are calculated as the sum of the package coefficients for the group of goods and the remaining individual parcel coefficients, identifying that an order should be split into several parcels. Wherein the parcel coefficient is used to calculate the number of goods that can be placed in a parcel, for example, if the parcel coefficient of goods is greater than or equal to 1, then there is one parcel for each goods; and if the goods parcel coefficient is less than 1, putting a parcel in a plurality of goods, and the sum of the goods parcel coefficients in the parcel is less than 1. For example, a standard consumable may hold 4 a items or 2B items or 1C items, i.e., a has a package factor of 1/4-0.25, B has a package factor of 1/2-0.5, and C has a package factor of 1/1-1, and if a user order contains three items ABC, the user order is split into 2 packages, package 1 contains item a and item B, and package 2 contains item C.

In some embodiments, the package goods packaging factor calculation rule is package length, width, height, default consumable volume. For example, the volume of the kit is 10cm³Default consumable volume 20cm³The package factor of the set goods is 10/20 ═ 0.5, i.e., two kits can be placed in one default consumable.

In the step, during parcel production, the parcel number of the order goods to be split is calculated according to the standard consumable volume and the goods parcel coefficient. The group goods are placed in the box, when the package quantity is calculated, the calculation is inaccurate according to a single SKU package coefficient, the group goods package coefficient needs to be set independently, the total package quantity to be split is calculated together according to the group goods package coefficient and the excess material single product package coefficient, and the consumable waste is reduced.

At step 260, picking tasks are generated.

In some embodiments, the picking task includes one or more sortation entries, each sortation entry including a SKU combination or an individual SKU that matches thereto.

In some embodiments, for multiple customer orders, when grouped into a collective order splitting picking task, the same groups of virtual group bar codes are in one task, which facilitates production. Placing sortation items having the same SKU combination in a single task facilitates increased picking efficiency.

In step 270, picking is performed at the locations of the group package goods and the locations of the individual goods according to the picking tasks.

In some embodiments, as shown in fig. 11, goods located in the virtual stack storage area are picked, and enter the picking interface, bind the containers, and according to the task information, the picker scans the storage location code, the stack bar code, and inputs the quantity, and the picking is completed. If one order is split into a plurality of picking tasks, confluence production is needed.

At step 280, the order requiring a merge proceeds with the goods merge.

In some embodiments, the containers and racking stores are scanned and a merge racking is performed.

In step 290, order rechecking is completed, and the goods in the user order are packaged and delivered out of the warehouse according to the number of packages corresponding to the user order.

In some embodiments, for an order containing a virtual stack, the order is rechecked according to a container, the stack bar code or the goods code is scanned, the order is rechecked, and the shipment is taken out after the rechecking is completed.

In the embodiment, the cargoes are grouped according to the user requirements, each grouped cargo corresponds to one SKU combination, so that the combination is more flexible, the delivery efficiency is higher, and the diversion of the cargoes can not occur due to the fact that no new SKU is required to be created, namely the statistics of the SKU is more accurate. In addition, the order wrapping number is calculated according to the sum of the group goods wrapping coefficient and the residual single-product wrapping coefficient, and consumable waste can be reduced.

Fig. 12 is a schematic structural diagram of some embodiments of the cargo scheduling system of the present disclosure. The cargo scheduling system is, for example, a WMS, and includes a SKU determining unit 1210, a combination matching unit 1220, a sorting item generating unit 1230, and a sorting unit 1240.

The SKU determination unit 1210 is configured to obtain the type of SKU and the quantity of each SKU involved in the user order.

The combination matching unit 1220 is configured to determine whether the user order can match the SKU combination according to the SKU category and the number of each SKU.

In this embodiment, the order combination is calculated following the "stock minimization principle".

The sortation item generation unit 1230 is configured to generate one or more sortation items, each sortation item including a SKU combination or an individual SKU that is matched to, based on the matching results.

The sorting unit 1240 is configured to sort the goods according to the one or more sorting items.

In some embodiments, the sorting unit 1240 is further configured to determine, according to the matched one or more SKU combinations, an area where the group goods corresponding to each SKU combination is located; and determining the storage position of each group of goods according to at least one of the sequence of the production date of each group of goods from morning to evening and the sequence of the storage position inventory from small to large in the area of each group of goods.

In the above embodiment, whether the SKU combination can be matched is determined according to the SKU type and the number of each SKU involved in the user order, then one or more sorting entries are generated according to the matching result, each sorting entry includes a matched SKU combination or a single SKU, and goods are sorted according to the one or more sorting entries, so that the sorting efficiency is improved, and the SKU counting accuracy can also be improved.

Fig. 13 is a schematic structural diagram of another embodiment of the cargo scheduling system of the present disclosure. The cargo scheduling system further includes a pre-configuration unit 1310 configured to pre-group the cargo according to the user's requirement, and generate a plurality of grouped cargo, each grouped cargo corresponding to a SKU group, and each SKU group includes the type of SKU and the number of each SKU.

In the embodiment, the cargoes are grouped according to the user requirements, and each grouped cargo corresponds to one SKU combination, so that the combination is more flexible, and the delivery efficiency is higher.

In other embodiments of the present disclosure, the cargo dispatching system further comprises a parcel number determination unit 1320 and a packing unit 1330.

The parcel number determination unit 1320 is configured to determine the parcel number corresponding to the user order according to the sum of the parcel coefficient of the group of the matched one or more SKU combinations corresponding to each SKU combination and the parcel coefficient of the single item corresponding to each single item SKU.

The packing unit 1330 is configured to pack the goods in the user order according to the number of packages corresponding to the user order.

In the embodiment, during the production of the packages, the number of the packages to be split for the order goods is calculated according to the standard consumable volume and the goods package coefficient. The group goods are placed in the box, when the package quantity is calculated, the calculation is inaccurate according to a single SKU package coefficient, the group goods package coefficient needs to be set independently, the total package quantity to be split is calculated together according to the group goods package coefficient and the excess material single product package coefficient, and the consumable waste is reduced.

Fig. 14 is a schematic structural diagram of another embodiment of the cargo scheduling system of the present disclosure. The cargo scheduling system 1400 includes a memory 1410 and a processor 1420. Wherein: the memory 1410 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory 1410 is used to store instructions in the embodiments corresponding to fig. 1-3. Processor 1420 is coupled to memory 1410 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 1420 is used to execute instructions stored in memory.

In some embodiments, processor 1420 is coupled to memory 1410 through BUS BUS 1430. The cargo deployment system 1400 may also be coupled to an external storage system 1450 via a storage interface 1440 for purposes of invoking external data, and may also be coupled to a network or another computer system (not shown) via a network interface 1460. And will not be described in detail herein.

In the embodiment, the data instructions are stored in the memory and processed by the processor, so that the sorting efficiency is improved, and the accuracy of SKU statistics can be improved.

In other embodiments, a computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the embodiments corresponding to fig. 1-3. As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Thus far, the present disclosure has been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.

Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A cargo scheduling method comprising:

acquiring the types of stock keeping units SKU related to the user order and the quantity of each type of SKU;

judging whether the user order can match with the SKU combination or not according to the SKU types and the quantity of each SKU;

generating one or more sorting entries according to the matching result, wherein each sorting entry comprises a SKU combination or a single SKU matched with the sorting entry; and

and sorting the goods according to the one or more sorting items.

2. The cargo scheduling method of claim 1, further comprising:

according to the user requirements, the goods are grouped in advance to generate a plurality of grouped goods, each grouped goods corresponds to a SKU combination, and each SKU combination comprises the type of the SKU and the quantity of each SKU.

3. The cargo scheduling method according to claim 1,

if the user order can match a plurality of SKU combinations, preferentially taking the SKU combination which meets the requirement of the user order and contains the maximum SKU quantity in the SKU combinations as the SKU combination matched with the user order; and

and judging whether the SKU combination which meets the residual goods requirement in the user order and contains the maximum SKU quantity exists in the plurality of SKU combinations except the SKU combination matched with the user order, if so, taking the SKU combination which meets the residual goods requirement in the user order and contains the maximum SKU quantity as the SKU combination matched with the user order, and continuing to carry out the step of judging whether the SKU combination which meets the residual goods requirement in the user order and contains the maximum SKU quantity exists in the plurality of SKU combinations except the SKU combination matched with the user order, otherwise, taking the SKU corresponding to the residual goods in the user order as the single SKU.

4. The cargo scheduling method of claim 1, further comprising:

determining the area of the set of goods corresponding to each SKU combination according to the matched one or more SKU combinations; and

and determining the storage position of each group of goods according to at least one of the sequence of the production date of each group of goods from morning to evening and the sequence of the storage position inventory from small to large in the area of each group of goods.

5. The cargo scheduling method according to any one of claims 1 to 4, further comprising:

determining the parcel number corresponding to the user order according to the sum of the parcel coefficient of the group goods corresponding to each SKU combination and the parcel coefficient of the single goods corresponding to each single SKU in the matched one or more SKU combinations; and

and packaging the goods in the user order according to the number of the packages corresponding to the user order.

6. The cargo scheduling method according to claim 5,

the packaging coefficient of the group goods is the ratio of the box volume of the group goods to the default consumable volume.

7. A cargo scheduling system comprising:

a SKU determination unit configured to acquire a SKU type of stock keeping unit and a quantity of each SKU involved in the user order;

the combination matching unit is configured to judge whether the user order can match the SKU combination according to the SKU type and the quantity of each SKU;

a sorting item generating unit configured to generate one or more sorting items according to the matching result, wherein each sorting item comprises a SKU combination or a single SKU matched with the sorting item; and

a sorting unit configured to sort the goods according to the one or more sorting items.

8. The cargo scheduling system of claim 7, further comprising:

the pre-configuration unit is configured to pre-group the goods according to user requirements to generate a plurality of group goods, each group goods corresponds to a SKU combination, and each SKU combination comprises the type of the SKU and the quantity of each SKU.

9. The cargo scheduling system according to claim 7 or 8, further comprising:

the parcel number determining unit is configured to determine the parcel number corresponding to the user order according to the sum of the parcel coefficient of the group of goods corresponding to each SKU combination and the parcel coefficient of the single goods corresponding to each single SKU in the matched one or more SKU combinations; and

and the packing unit is configured to pack the goods in the user order according to the number of the packages corresponding to the user order.

10. A cargo scheduling system comprising:

a memory; and

a processor coupled to the memory, the processor configured to perform the cargo scheduling method of any of claims 1-6 based on instructions stored in the memory.

11. A non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the cargo scheduling method of any one of claims 1 to 6.